{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Clip Search",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "Q2bAyMVJlWA9",
        "outputId": "93c3a108-74b4-4773-8aa2-e21796cccfd4"
      },
      "source": [
        "#@title restart after running this cell\n",
        "import subprocess\n",
        "\n",
        "CUDA_version = [s for s in subprocess.check_output([\"nvcc\", \"--version\"]).decode(\"UTF-8\").split(\", \") if s.startswith(\"release\")][0].split(\" \")[-1]\n",
        "print(\"CUDA version:\", CUDA_version)\n",
        "\n",
        "if CUDA_version == \"10.0\":\n",
        "    torch_version_suffix = \"+cu100\"\n",
        "elif CUDA_version == \"10.1\":\n",
        "    torch_version_suffix = \"+cu101\"\n",
        "elif CUDA_version == \"10.2\":\n",
        "    torch_version_suffix = \"\"\n",
        "else:\n",
        "    torch_version_suffix = \"+cu110\"\n",
        "!git clone https://github.com/kingchloexx/CLIP-Image-Classification\n",
        "! pip install torch==1.7.1{torch_version_suffix} torchvision==0.8.2{torch_version_suffix} -f https://download.pytorch.org/whl/torch_stable.html ftfy regex\n",
        "!pip install ftfy\n",
        "!git clone https://github.com/kingchloexx/CLIP-Image-Classification.git\n",
        "%cd CLIP-Image-Classification"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CUDA version: 11.0\n",
            "Cloning into 'CLIP-Image-Classification'...\n",
            "remote: Enumerating objects: 101, done.\u001b[K\n",
            "remote: Counting objects: 100% (101/101), done.\u001b[K\n",
            "remote: Compressing objects: 100% (84/84), done.\u001b[K\n",
            "remote: Total 101 (delta 51), reused 43 (delta 15), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (101/101), 2.16 MiB | 31.54 MiB/s, done.\n",
            "Resolving deltas: 100% (51/51), done.\n",
            "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
            "Collecting torch==1.7.1+cu110\n",
            "\u001b[?25l  Downloading https://download.pytorch.org/whl/cu110/torch-1.7.1%2Bcu110-cp37-cp37m-linux_x86_64.whl (1156.8MB)\n",
            "\u001b[K     |███████████████████████         | 834.1MB 1.4MB/s eta 0:03:50tcmalloc: large alloc 1147494400 bytes == 0x5566d1518000 @  0x7fd791cf5615 0x55669733706c 0x556697416eba 0x556697339e8d 0x55669742b99d 0x5566973adfe9 0x5566973a8b0e 0x55669733b77a 0x5566973ade50 0x5566973a8b0e 0x55669733b77a 0x5566973aa86a 0x55669742c7c6 0x5566973a9ee2 0x55669742c7c6 0x5566973a9ee2 0x55669742c7c6 0x5566973a9ee2 0x55669742c7c6 0x5566974ae431 0x55669740f049 0x556697379c84 0x55669733a8e9 0x5566973aeade 0x55669733b69a 0x5566973a9a45 0x5566973a8e0d 0x55669733b77a 0x5566973a9a45 0x55669733b69a 0x5566973a9a45\n",
            "\u001b[K     |█████████████████████████████▏  | 1055.7MB 1.3MB/s eta 0:01:21tcmalloc: large alloc 1434370048 bytes == 0x556715b6e000 @  0x7fd791cf5615 0x55669733706c 0x556697416eba 0x556697339e8d 0x55669742b99d 0x5566973adfe9 0x5566973a8b0e 0x55669733b77a 0x5566973ade50 0x5566973a8b0e 0x55669733b77a 0x5566973aa86a 0x55669742c7c6 0x5566973a9ee2 0x55669742c7c6 0x5566973a9ee2 0x55669742c7c6 0x5566973a9ee2 0x55669742c7c6 0x5566974ae431 0x55669740f049 0x556697379c84 0x55669733a8e9 0x5566973aeade 0x55669733b69a 0x5566973a9a45 0x5566973a8e0d 0x55669733b77a 0x5566973a9a45 0x55669733b69a 0x5566973a9a45\n",
            "\u001b[K     |████████████████████████████████| 1156.7MB 1.2MB/s eta 0:00:01tcmalloc: large alloc 1445945344 bytes == 0x55676b35a000 @  0x7fd791cf5615 0x55669733706c 0x556697416eba 0x556697339e8d 0x55669742b99d 0x5566973adfe9 0x5566973a8b0e 0x55669733b77a 0x5566973a9c9e 0x5566973a8b0e 0x55669733b77a 0x5566973a9c9e 0x5566973a8b0e 0x55669733b77a 0x5566973a9c9e 0x5566973a8b0e 0x55669733b77a 0x5566973a9c9e 0x5566973a8b0e 0x55669733b77a 0x5566973a9c9e 0x55669733b69a 0x5566973a9c9e 0x5566973a8b0e 0x55669733b77a 0x5566973aa86a 0x5566973a8b0e 0x55669733b77a 0x5566973aa86a 0x5566973a8b0e 0x55669733be11\n",
            "\u001b[K     |████████████████████████████████| 1156.8MB 16kB/s \n",
            "\u001b[33mWARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ProtocolError('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))': /simple/torchvision/\u001b[0m\n",
            "\u001b[?25hCollecting torchvision==0.8.2+cu110\n",
            "\u001b[?25l  Downloading https://download.pytorch.org/whl/cu110/torchvision-0.8.2%2Bcu110-cp37-cp37m-linux_x86_64.whl (12.9MB)\n",
            "\u001b[K     |████████████████████████████████| 12.9MB 816kB/s \n",
            "\u001b[?25hCollecting ftfy\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/04/06/e5c80e2e0f979628d47345efba51f7ba386fe95963b11c594209085f5a9b/ftfy-5.9.tar.gz (66kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 8.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (2019.12.20)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch==1.7.1+cu110) (1.19.5)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.7.1+cu110) (3.7.4.3)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision==0.8.2+cu110) (7.0.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from ftfy) (0.2.5)\n",
            "Building wheels for collected packages: ftfy\n",
            "  Building wheel for ftfy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ftfy: filename=ftfy-5.9-cp37-none-any.whl size=46451 sha256=cb01253126b55f6b1dbf5ff0bb8a35efdeac61ebd1056e54c76ab3c3b969b670\n",
            "  Stored in directory: /root/.cache/pip/wheels/5e/2e/f0/b07196e8c929114998f0316894a61c752b63bfa3fdd50d2fc3\n",
            "Successfully built ftfy\n",
            "\u001b[31mERROR: torchtext 0.9.0 has requirement torch==1.8.0, but you'll have torch 1.7.1+cu110 which is incompatible.\u001b[0m\n",
            "Installing collected packages: torch, torchvision, ftfy\n",
            "  Found existing installation: torch 1.8.0+cu101\n",
            "    Uninstalling torch-1.8.0+cu101:\n",
            "      Successfully uninstalled torch-1.8.0+cu101\n",
            "  Found existing installation: torchvision 0.9.0+cu101\n",
            "    Uninstalling torchvision-0.9.0+cu101:\n",
            "      Successfully uninstalled torchvision-0.9.0+cu101\n",
            "Successfully installed ftfy-5.9 torch-1.7.1+cu110 torchvision-0.8.2+cu110\n",
            "Requirement already satisfied: ftfy in /usr/local/lib/python3.7/dist-packages (5.9)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from ftfy) (0.2.5)\n",
            "fatal: destination path 'CLIP-Image-Classification' already exists and is not an empty directory.\n",
            "/content/CLIP-Image-Classification\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "cellView": "form",
        "id": "1EVi_0JMlgb1",
        "outputId": "5d68af2e-5d5b-47a0-e998-7dde84307913"
      },
      "source": [
        "#@title search image\n",
        "import numpy as np\n",
        "from scipy.ndimage.filters import gaussian_filter\n",
        "from PIL import Image\n",
        "from classify import load, classify\n",
        "\n",
        "image_url = \"https://blobcdn.same.energy/a/0f/1f/0f1f99ce70c02cbe63813cf345cc3a83ae2efffc\"#@param {type:\"string\"}\n",
        "search = \"eye\"#@param {type:\"string\"}\n",
        "things=[search]\n",
        "\n",
        "\n",
        "load(things)\n",
        "blocks = []\n",
        "scores = []\n",
        "rescale = 512#@param\n",
        "chunk_size=128#@param\n",
        "\n",
        "!wget \"$image_url\" -O \"/content/input.jpg\" -q\n",
        "image = Image.open(\"/content/input.jpg\")\n",
        "w,h = image.size\n",
        "image = image.resize((rescale,rescale))\n",
        "npimg = np.array(image)\n",
        "big_chunks=True#@param {type:\"boolean\"}\n",
        "\n",
        "def block(x,y):\n",
        "    b = []\n",
        "    for i in range(chunk_size-1):\n",
        "        b.append(npimg[x+i][y:y+chunk_size])\n",
        "    b = np.array(b)\n",
        "    b = Image.fromarray(b)\n",
        "    b.save(\"image.png\")\n",
        "    return b, classify(\"image.png\", return_raw=True)[0]\n",
        "blocks = []\n",
        "scores = []\n",
        "ii = []\n",
        "jj = []\n",
        "# top row\n",
        "\n",
        "if(big_chunks):\n",
        "    iterate = int(size/chunk_size-1)\n",
        "else:\n",
        "    iterate = rescale\n",
        "for i in range(iterate):\n",
        "    for j in range(iterate):\n",
        "        if(big_chunks):\n",
        "            b,c = block(i*chunk_size,j*chunk_size)\n",
        "            ii.append(i*chunk_size)\n",
        "            jj.append(j*chunk_size)\n",
        "        else:\n",
        "            b,c = block(i,j)\n",
        "            ii.append(i)\n",
        "            jj.append(j)\n",
        "        blocks.append(b)\n",
        "        scores.append(c)\n",
        "best_index = scores.index(max(scores)) \n",
        "iii = ii[best_index]\n",
        "jjj = jj[best_index]\n",
        "score = scores[best_index]\n",
        "print(\"top left x: {} | top left y {} | similarity: {}\".format(iii,jjj,score))\n",
        "blocks[scores.index(max(scores))].resize((int(w/8)*4,int(h/8)*4))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "top left x: 128 | top left y 128 | similarity: 0.314208984375\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGBA size=240x240 at 0x7F32B034D3D0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    }
  ]
}